Automatic identification of medical information pertinent to a natural language conversation

ABSTRACT

A mechanism is provided for implementing an emergency response cognitive computing system. The emergency response cognitive computing system identifies a first party about which a communication is being performed between a caller and the emergency response system. The emergency response cognitive computing system generates, in real-time, a transcript of the communication as the communication is being conducted. The emergency response cognitive computing system performs natural language processing on the transcript of the communication to identify portions of content corresponding to medical concepts. The emergency response cognitive computing system processes patient information to identify elements of the patient information referencing concepts corresponding to medical concepts. The emergency response cognitive computing system outputs the elements of the patient information to a second party involved in the communication.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for automaticidentification of medical information pertinent to a natural languageconversation.

When a person contacts emergency services to indicate a medicalemergency, information about the person with whom the emergency pertainsis usually limited to answers provided to the questions asked byemergency personnel. These questions may include but are not limited to:an address of the emergency, the person's problem or the type ofincident, approximate age of the person, whether the person isconscious, whether the person is breathing, or the like.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising a processor and a memory, the memorycomprising instructions that are executed by the processor to configurethe processor to implement an emergency response cognitive computingsystem. The illustrative embodiment identities, by the emergencyresponse cognitive computing system, a first party about which acommunication is being performed between a caller and the emergencyresponse system. The illustrative embodiment generates, by the emergencyresponse cognitive computing system, in real-time, a transcript of thecommunication as the communication is being conducted. The illustrativeembodiment performs, by the emergency response cognitive computingsystem, natural language processing on the transcript of thecommunication to identify portions of content corresponding to medicalconcepts. The illustrative embodiment processes, by the emergencyresponse cognitive computing system, patient information to identifyelements of the patient information referencing concepts correspondingto medical concepts. The illustrative embodiment outputs, by theemergency response cognitive computing system, the elements of thepatient information to a second party involved in the communication.

In other illustrative embodiments, a computer program product comprisinga computer usable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example block diagram illustrating components of anemergency response cognitive computing system in accordance with oneillustrative embodiment;

FIG. 2 depicts a schematic diagram of one illustrative embodiment of acognitive healthcare system in a computer network;

FIG. 3 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented; and

FIG. 4 is a flowchart outlining example operations performed by anemergency response cognitive computing system in automaticallyidentifying medical information pertinent to a natural languageconversation and notifying medical personnel in accordance with oneillustrative embodiment.

DETAILED DESCRIPTION

When a person contacts emergency services to indicate a medicalemergency, often times the emergency personnel do not have access tomedical information pertaining to the call. For example, if a personcalls 911 and complains of chest pains, answers to questions such as: anaddress of the emergency, the person's problem or the type of incident,approximate age of the person, whether the person is conscious, whetherthe person is breathing, or the like, would not provide personal medicalhistory about the person. Thus, the illustrative embodiments recognizethat it would be beneficial to those responding to the call to knowwhether the particular person recently had heart surgery and wasrecently released from the hospital. In instances where persons arediscussing a medical situation, such as when a person calls 911 or otheremergency services, it would be helpful to be able to access personalmedical information that is pertinent to the natural languageconversation.

Therefore, the illustrative embodiments provide mechanisms forautomatically identifying medical information pertinent to the naturallanguage conversation and notifying medical personnel of the pertinentmedical information. The illustrative embodiments provide a cognitivesystem that analyzes a transcription of the natural languageconversation as it is occurring, such as by way of a chatbot typemechanism, and automatically retrieves pertinent medical informationabout one or more of the parties involved in the natural languageconversation based on the medical concepts referenced in the naturallanguage conversation for real-time presentation to one or more of theparties involved in the natural language conversation. Moreover, thepertinent medical information may also be forwarded to other partieswhich are associated with the natural language conversation beingconducted. For example, appropriate notifications may be sent to careproviders, such as emergency medical technicians (EMTs) on route, firstresponders, emergency room personnel waiting for the arrival of thepatient, or the like. In some cases, the cognitive system may retrieve aperson's electronic medical records (EMRs) and highlight pertinentportions for presentation to the appropriate personnel. Thus, theillustrative embodiments improve the ability of first responders toassist persons in emergency situations by automatically retrieving themost pertinent medical information needed by the first responders tounderstand an emergency situation and improve their ability to provideemergency care to one or more persons.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, the illustrative embodiments of the present inventionprovides a methodology, apparatus, system and computer program productfor automatically identifying medical information pertinent to a naturallanguage conversation between emergency personnel and a caller andnotifying medical personnel of the pertinent medical information. Thefollowing illustrates the operations of a cognitive system in analyzinga transcription of the natural language conversation between emergencypersonnel and one or more calling parties as it is occurring andautomatically retrieving pertinent medical information about the one ormore of the calling parties involved in the natural languageconversation based on the medical concepts referenced in the naturallanguage conversation. The cognitive system identifies the pertinentmedical information and then presents the pertinent medical informationin real-time to the emergency personnel involved in the natural languageconversation. Moreover, the pertinent medical information may also beforwarded to other emergency personnel that are associated with thenatural language conversation being conducted. For example, appropriatenotifications may be sent to care providers, such as emergency medicaltechnicians (EMTs) on route, first responders, emergency room personnelwaiting for the arrival of the patient, or the like. In some cases, thecognitive system may retrieve a person's electronic medical records(EMRs) and highlight pertinent portions for presentation to theappropriate personnel.

FIG. 1 is an example block diagram illustrating components of acognitive system for automatically identifying medical information of apatient pertinent to a natural language conversation between the patientand emergency personnel in accordance with one illustrative embodiment.As shown in FIG. 1, emergency response cognitive computing system 100comprises monitoring and transcription engine 102, medical conditionidentification engine 104, retrieval engine 106, and presentation engine108. Emergency response cognitive computing system 100 may be integratedwith an emergency response system which may provide further informationgathered by the emergency response system to assist in identifying theadditional medical information to be retrieved and presented.

In operation, as a real-time communication is conducted between callingparty 110 and emergency personnel 112. For example, emergency responsecognitive computing system 100 may be integrated in, or otherwiseoperate in conjunction with, a 911 response computing system or thelike, which may itself collect information about persons contacting theemergency service, e.g., caller identification information, informationabout callers entered manually by emergency personnel 112, such as anaddress of the emergency, a name of the patient (which may not be thesame as calling party 110), a medical issue being experienced by thepatient, approximate age of the patient, whether the patient isconscious, whether the patient is breathing, and the like. Emergencypersonnel 112 may enter this information into an appropriate interfaceof the 911 computer system and thus, emergency response cognitivecomputing system 100. Alternatively, an identity of the person orpersons that is the subject of the call may be automatically determinedfrom identifiers automatically identified by the computer system itself,e.g., automated caller ID information.

Thus, monitoring and transcription engine 102 monitors the real-timecommunication between calling party 110 and emergency personnel 112 aswell as any data automatically identified. For the entire real-timecommunication, monitoring and transcription engine 102 transcribes thereal-time communication as well as any automatically identified datainto a real-time transcription file. As monitoring and transcriptionengine 102 is transcribing the real-time communication into thereal-time transcription file, medical condition identification engine104 performs natural language processing on the text of the real-timetranscription file to identify terms/phrases indicative of medicalconditions identified in the real-time transcription file for which userspecific medical information is desirable to provide a completeunderstanding of the context of the real-time communication.

In order to identify terms/phrases indicative of medical conditionsidentified in the real-time transcription file for which user specificmedical information is desirable to provide a complete understanding ofthe context of the real-time communication, medical conditionidentification engine 104 compares each identified term or set of terms(i.e. a phrase) to one or more terms or set of terms in medicalcondition library 114, which may be a medical condition library such asthe Unified Medical Language System (UMLS) that is a set of files andsoftware that brings together many health and biomedical vocabulariesand standards to enable interoperability between computer systems. Foreach medical condition identified in the real-time transcription file,retrieval engine 106 searches for associated medical information fromelectronic medical records (EMRs) 116 associated with the patientidentified by calling party 110. Thus, retrieval engine 106 identifiesportions of the patient's information that is most relevant to theissues being discussed during the real-time communication through theperformance of emergency response cognitive computing system 100. Forexample, if calling party 110 identifies that the patient is complainingof chest pains, then this may be used to search patient EMRs 116 todetermine if the patient has had any medical conditions, procedures,symptoms, or the like, that are related to chest pains. Thus, if thepatient has recently had a heart attack, then this information may beidentified in patient EMRs.

Responsive to retrieval engine 106 identifying any associated medicalinformation from electronic medical records (EMRs) 116 associated withthe patient, presentation engine 108 highlights or otherwise presentsthe associated medical information to one or more emergency personnel,such as emergency personnel 112 and/or other emergency personnel thatare designated as authorized recipients of such information, which maybe determined dynamically at the time of the conversation, e.g., EMTsassigned to respond, doctors, nurses, or other medical personnel havebeen assigned to the case, or the like, which may include emergency roomdoctors, nurses, or other medical personnel, the patient's personaldoctor, or the like. The highlighting or otherwise presenting theassociated medical information to one or more emergency personnel mayinvolve sending such information from the emergency system to othercomputing systems or communication systems remotely located from theemergency system.

It is clear from the above, that the illustrative embodiments may beutilized in many different types of data processing environments. Inorder to provide a context for the description of the specific elementsand functionality of the illustrative embodiments, FIGS. 2-3 areprovided hereafter as example environments in which aspects of theillustrative embodiments may be implemented. It should be appreciatedthat FIGS. 2-3 are only examples and are not intended to assert or implyany limitation with regard to the environments in which aspects orembodiments of the present invention may be implemented. Manymodifications to the depicted environments may be made without departingfrom the spirit and scope of the present invention.

FIGS. 2-3 are directed to describing an example cognitive system forpresenting patient associated medical information to be utilized byemergency personnel responding to a medical issue of the patient thatimplements a request processing pipeline, request processingmethodology, and request processing computer program product with whichthe mechanisms of the illustrative embodiments are implemented. Theserequests may be provided as structure request messages, unstructuredrequest messages or any other suitable format for requesting anoperation to be performed by the cognitive system. As described in moredetail hereafter, the particular application that is implemented in thecognitive system of the present invention is an application forpresenting contextually relevant patient data in relation to otherpatients to a medical professional in a graphical user interface.

It should be appreciated that the cognitive system, while shown ashaving a single request processing pipeline in the examples hereafter,may in fact have multiple request processing pipelines. Each requestprocessing pipeline may be separately trained and/or configured toprocess requests associated with different domains or be configured toperform the same or different analysis on input requests, depending onthe desired implementation. For example, in some cases, a first requestprocessing pipeline may be trained to operate on input requests directedto identifying a medical condition based on transcripts of a real-timecommunication between a calling party and emergency personnel. In othercases, for example, the request processing pipelines may be configuredto identify associated medical information front a patient's electronicmedical records based on an identified medical condition from areal-time communication between a calling party and emergency personnel.

Moreover, each request processing pipeline may have its own associatedcorpus or corpora that they ingest and operate on, e.g., one corpus formedical condition documents and another corpus for electronic medicalrecord documents in the above examples. In some cases, the requestprocessing pipelines may each operate on the same domain of requests butmay have different configurations, e.g., different annotators ordifferently trained annotators, such that different analysis andpotential responses are generated. The cognitive system may provideadditional logic for routing requests to the appropriate requestprocessing pipeline, such as based on a determined domain of the inputrequest, combining and evaluating final results generated by theprocessing performed by multiple request processing pipelines, and othercontrol and interaction logic that facilitates the utilization ofmultiple request processing pipelines.

It should be appreciated that while the present invention will bedescribed in the context of the cognitive system implementing one ormore request processing pipelines that operate on a request, theillustrative embodiments are not limited to such. Rather, the mechanismsof the illustrative embodiments may operate on requests that are posedas “questions” or formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof the request processing pipeline with regard to identifying issuesassociated with the medical treatments for the medical condition of thepatent and presenting a corresponding alerts or notifications. Forexample, identifying one or more medical conditions of the patient thatinclude a medication that the patient experienced an adverse reactionto, a medication that has caused unwanted side effects, a medicationthat has not helped control the patient's medical condition, or thelike.

It should be appreciated that the mechanisms described in FIGS. 2-3 areonly examples and are not intended to state or imply any limitation withregard to the type of cognitive system mechanisms with which theillustrative embodiments are implemented. Many modifications to theexample cognitive system shown in FIGS. 2-3 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, for example, and machine learning logic, which may beprovided as specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.The logic of the cognitive system implements the cognitive operation(s),examples of which include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches.

IBM Watson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like high accuracy at speeds far faster than human beings andon a larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding,    -   Ingest and process vast amounts of structured and unstructured        data,    -   Generate and evaluate hypothesis,    -   Weigh and evaluate responses that are based only on relevant        evidence,    -   Provide situation-specific advice, insights, and guidance,    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes,    -   Enable decision making at the point of impact (contextual        guidance),    -   Scale in proportion to the task,    -   Extend and magnify human expertise and cognition,    -   Identify resonating, human-like attributes and traits from        natural language,    -   Deduce various language specific or agnostic attributes from        natural language,    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall),    -   Predict and sense with situational awareness that mimic human        cognition based on experiences, or    -   Answer questions based on natural language and specific        evidence.

In one aspect, cognitive systems provide mechanisms for responding torequests posed to these cognitive systems using a request processingpipeline and/or process requests which may or may not be posed asnatural language requests. The requests processing pipeline is anartificial intelligence application executing on data processinghardware that responds to requests pertaining to a given subject-matterdomain presented in natural language. The request processing pipelinereceives inputs from various sources including input over a network, acorpus of electronic documents or other data, data from a contentcreator, information from one or more content users, and other suchinputs from other possible sources of input. Data storage devices storethe corpus of data. A content creator creates content in a document foruse as part of a corpus of data with the request processing pipeline.The document may include any file, text, article, or source of data foruse in the requests processing system. For example, a request processingpipeline accesses a body of knowledge about the domain, or subjectmatter area, e.g., financial domain, medical domain, legal domain, etc.,where the body of knowledge (knowledgebase) can be organized in avariety of configurations, e.g., a structured repository ofdomain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input requests to cognitive system which implements therequest processing pipeline. The request processing pipeline thenresponds to the requests using the content in the corpus of data byevaluating documents, sections of documents, portions of data in thecorpus, or the like. When a process evaluates a given section of adocument for semantic content, the process can use a variety ofconventions to query such document from the request processing pipeline,e.g., sending the query to the request processing pipeline as awell-formed requests which is then interpreted by the request processingpipeline and a response is provided containing one or more responses tothe request. Semantic content is content based on the relation betweensignifiers, such as words, phrases, signs, and symbols, and what theystand for, their denotation, or connotation. In other words, semanticcontent is content that interprets an expression, such as by usingNatural Language Processing.

As will be described in greater detail hereafter, the request processingpipeline receives a request, parses the request to extract the majorfeatures of the request, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the requestprocessing pipeline generates a set of responses to the request, bylooking across the corpus of data for portions of the corpus of datathat have some potential for containing a valuable response to therequest. The request processing pipeline then performs deep analysis onthe language of the request and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the request and the found portions ofthe corpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

As mentioned above, request processing pipeline mechanisms operate byaccessing information from a corpus of data or information (alsoreferred to as a corpus of content), analyzing it, and then generatinganswer results based on the analysis of this data. Accessing informationfrom a corpus of data typically includes: a database query that answersrequests about what is in a collection of structured records, and asearch that delivers a collection of document links in response to aquery against a collection of unstructured data (text, markup language,etc.). Conventional request processing systems are capable of generatinganswers based on the corpus of data and the input request, verifyinganswers to a collection of request for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting responsesto requests from a pool of potential answers, i.e. candidate answers.

FIG. 2 depicts a schematic diagram of one illustrative embodiment of acognitive system 200 implementing a request processing pipeline 208,which in some embodiments may be a request processing pipeline, in acomputer network 202. For purposes of the present description, it willbe assumed that the request processing pipeline 208 is implemented as arequest processing pipeline that operates on structured and/orunstructured requests in the form of input questions. One example of aquestion processing operation which may be used in conjunction with theprinciples described herein is described in U.S. Patent ApplicationPublication No. 2011/0125734, which is herein incorporated by referencein its entirety. The cognitive system 200 is implemented on one or morecomputing devices 204A-D (comprising one or more processors and one ormore memories, and potentially any other computing device elementsgenerally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 202. For purposes of illustration only, FIG. 2 depicts thecognitive system 200 being implemented on computing device 204A only,but as noted above the cognitive system 200 may be distributed acrossmultiple computing devices, such as a plurality of computing devices204A-D. The network 202 includes multiple computing devices 204A-D,which may operate as server computing devices, and 210-212 which mayoperate as client computing devices, in communication with each otherand with other devices or components via one or more wired and/orwireless data communication links, where each communication linkcomprises one or more of wires, routers, switches, transmitters,receivers, or the like. In some illustrative embodiments, the cognitivesystem 200 and network 202 enables question processing and answergeneration (QA) functionality for one or more cognitive system users viatheir respective computing devices 210-212. In other embodiments, thecognitive system 200 and network 202 may provide other types ofcognitive operations including, but not limited to, request processingand cognitive response generation which may take many different formsdepending upon the desired implementation, e.g., cognitive informationretrieval, training/instruction of users, cognitive evaluation of data,or the like. Other embodiments of the cognitive system 200 may be usedwith components, systems, sub-systems, and/or devices other than thosethat are depicted herein.

The cognitive system 200 is configured to implement a request processingpipeline 208 that receive inputs from various sources. The requests maybe posed in the form of a natural language question, natural languagerequest for information, natural language request for the performance ofa cognitive operation, or the like. For example, the cognitive system200 receives input from the network 202, a corpus or corpora ofelectronic documents 206, cognitive system users, and/or other data andother possible sources of input. In one embodiment, some or all of theinputs to the cognitive system 200 are routed through the network 202.The various computing devices 204A-D on the network 202 include accesspoints for content creators and cognitive system users. Some of thecomputing devices 204A-D includes devices for a database storing thecorpus or corpora of data 206 (which is shown as a separate entity inFIG. 2 for illustrative purposes only). Portions of the corpus orcorpora of data 206 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 2. The network 202 includes localnetwork connections and remote connections in various embodiments, suchthat the cognitive system 200 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus or corpora of data 206 for use as part of a corpus of datawith the cognitive system 200. The document includes any file, text,article, or source of data for use in the cognitive system 200.Cognitive system users access the cognitive system 200 via a networkconnection or an Internet connection to the network 202, and requests tothe cognitive system 200 that are responded to/processed based on thecontent in the corpus or corpora of data 206. In one embodiment, therequests are formed using natural language. The cognitive system 200parses and interprets the request via a pipeline 208, and provides aresponse to the cognitive system user, e.g., cognitive system user 210,containing one or more responses to the request posed, response to therequest, results of processing the request, or the like. In someembodiments, the cognitive system 200 provides a response to users in aranked list of candidate answers/responses while in other illustrativeembodiments, the cognitive system 200 provides a single final responseor a combination of a response and ranked listing of other candidateresponses.

The cognitive system 200 implements the pipeline 208 which comprises aplurality of stages for processing a request based on informationobtained from the corpus or corpora of data 206. The pipeline 208generates responses for the request based on the processing of therequest and the corpus or corpora of data 206.

In some illustrative embodiments, the cognitive system 200 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a pipeline of the IBM Watson™ cognitive systemreceives a request which it then parses to extract the major features ofthe request, which in turn are then used to formulate queries that areapplied to the corpus or corpora of data 206. Based on the applicationof the queries to the corpus or corpora of data 206, a set ofhypotheses, or candidate responses to the request, are generated bylooking across the corpus or corpora of data 206 for portions of thecorpus or corpora of data 206 (hereafter referred to simply as thecorpus 206) that have some potential for containing a valuable responseto the response. The pipeline 208 of the IBM Watson™ cognitive systemthen performs deep analysis on the language of the request and thelanguage used in each of the portions of the corpus 206 found during theapplication of the queries using a variety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the pipeline 208 of the IBM Watson™ cognitive system200, in this example, has regarding the evidence that the potentialcandidate answer is interred by the request. This process is repeatedfor each of the candidate answers to generate a ranked listing ofcandidate answers which may then be presented to the user that submittedthe request, e.g., a user of client computing device 210, or from whicha final response is selected and presented to the user. More informationabout the pipeline 208 of the IBM Watson™ cognitive system 200 may beobtained, for example, from the IBM Corporation website, IBM Redbooks,and the like. For example, information about the pipeline of the IBMWatson™ cognitive system can be found in Yuan et al., “Watson andHealthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems:An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As noted above, while the input to the cognitive system 200 from aclient device may be posed in the form of a natural language request,the illustrative embodiments are not limited to such. Rather, therequest may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a healthcare based cognitive system, this analysis may involveprocessing patient medical records, medical guidance documentation fromone or more corpora, and the like, to provide a healthcare orientedcognitive system result. In particular, the mechanisms of the healthcarebased cognitive system may process drug-adverse events or adverse drugreaction pairings when performing the healthcare oriented cognitivesystem result, e.g., a diagnosis or treatment recommendation.

In the context of the present invention, cognitive system 200 mayprovide a cognitive functionality for assisting with healthcare basedoperations. For example, depending upon the particular implementation,the healthcare based operations may comprise patient diagnostics,medical treatment recommendation systems, personal patient care plangeneration and monitoring, patient electronic medical record (EMR)evaluation for various purposes, such as for identifying patients thatare suitable for a medical trial or a particular type of medicaltreatment, or the like. Thus, the cognitive system 200 may be ahealthcare cognitive system 200 that operates in the medical orhealthcare type domains and which may process requests for suchhealthcare operations via the request processing pipeline 208 input aseither structured or unstructured requests, natural language inputrequests, or the like. In one illustrative embodiment, the cognitivesystem 200 is an emergency response cognitive computing system thatpresents associated patient medical information to be utilized byemergency personnel responding to a medical issue of the patient.

As shown in FIG. 2, the cognitive system 200 is further augmented, inaccordance with the mechanisms of the illustrative embodiments, toinclude logic implemented in specialized hardware, software executed onhardware, or any combination of specialized hardware and softwareexecuted on hardware, for implementing an emergency response cognitivecomputing system 100. As described previously, the emergency responsecognitive computing system 100 performs an identification of medicalconditions from a transcription of a real-time communication between acalling party and emergency personnel and presents associated patientmedical information to be utilized by emergency personnel responding toa medical issue of a patient.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 3 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 3 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 300 is an example of a computer, such as server 204A or client210 in FIG. 2, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention are located. In one illustrative embodiment, FIG. 3 representsa server computing device, such as a server 204, which, which implementsa cognitive system 200 and QA system pipeline 208 augmented to includethe additional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 300 employs a hubarchitecture including North Bridge and Memory Controller Hub (NB/MCH)302 and South Bridge and Input/Output (I/O) Controller Hub (SB/ICH) 304.Processing unit 306, main memory 308, and graphics processor 310 areconnected to NB/MCH 302. Graphics processor 310 is connected to NB/MCH302 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 312 connectsto SB/ICH 304. Audio adapter 316, keyboard and mouse adapter 320, modem322, read only memory (ROM) 324, hard disk drive (HDD) 326, CD-ROM drive330, universal serial bus (USB) ports and other communication ports 332,and PCI/PCIe devices 334 connect to SB/ICH 304 through bus 338 and bus340. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while Pete does not. ROM 324 may be, for example, a flashbasic input/output system (BIOS).

HDD 326 and CD-ROM drive 330 connect to SB/ICH 304 through bus 340, HDD326 and CD-ROM drive 330 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 336 is connected to SB/ICH 304.

An operating system runs on processing unit 306. The operating systemcoordinates and provides control of various components within the dataprocessing system 300 in FIG. 3. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 10®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 300.

As a server, data processing system 300 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 300 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 306.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 326, and are loaded into main memory 308 for execution byprocessing unit 306. The processes for illustrative embodiments of thepresent invention are performed by processing unit 306 using computerusable program code, which is located in a memory such as, for example,main memory 308, ROM 324, or in one or more peripheral devices 326 and330, for example.

A bus system, such as bus 338 or bus 340 as shown in FIG. 3, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 322 or network adapter 312 of FIG. 3, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 308, ROM 324, or a cache such as found in NB/MCH 302 in FIG. 3.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 2 and 3 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 2and 3. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 300 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 300 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 300 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 4 is a flowchart outlining example operations performed by anemergency response cognitive computing system in automaticallyidentifying medical information pertinent to a natural languageconversation and notifying medical personnel in accordance with oneillustrative embodiment. As the exemplary operation begins, theemergency response cognitive computing system identifies a first partyabout which a communication is being performed between a caller and theemergency response system (step 402). The emergency response cognitivecomputing system retrieves patient information corresponding to thefirst party (step 404). The emergency response cognitive computingsystem generates, in real-time, a transcript of the communication as thecommunication is being conducted (step 406). The emergency responsecognitive computing system performs natural language processing on thetranscript of the communication to identify portions of contentcorresponding to medical concepts (step 408). The emergency responsecognitive computing system processes the patient information to identifyelements of the patient information referencing concepts correspondingto medical concepts (step 410). The emergency response cognitivecomputing system then outputs the elements of the patient information toa second party involved in the communication (step 412), with theoperation ending thereafter.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisingat least one processor and at least one memory, wherein the at least onememory comprises instructions that are executed by the at least oneprocessor to configure the at least one processor to implement anemergency response cognitive computing system, the method comprising:identifying, by the emergency response cognitive computing system, afirst party about which a communication is being performed between acaller and the emergency response system; generating, by the emergencyresponse cognitive computing system, in real-time, a transcript of thecommunication as the communication is being conducted; performing, bythe emergency response cognitive computing system, natural languageprocessing on the transcript of the communication to identify portionsof content corresponding to medical concepts, wherein the portions ofcontent corresponding to the medical concepts from the transcript of thecommunication are identified by comparing each term or each set of termsin the transcript of the communication to one or more terms or set ofterms in a medical condition library; processing, by the emergencyresponse cognitive computing system, patient information to identifyelements of the patient information referencing concepts correspondingto the medical concepts; and outputting, by the emergency responsecognitive computing system, the elements of the patient information to asecond party involved in the communication.
 2. The method of claim 1,wherein the first party is identified based on information entered intothe emergency response system based on information provided by thecaller.
 3. The method of claim 1, wherein the first party is identifiedbased on automated caller identification.
 4. The method of claim 1,wherein the emergency response cognitive computing system is integratedinto the emergency response system.
 5. The method of claim 1, whereinthe patient information is electronic medical records of the patient. 6.The method of claim 1, wherein the second party is one or more of anemergency personnel on the other end of the communication, an emergencymedical technician assigned to respond to the first party, a medicalprofessional assigned to treat the patient once transported to anmedical facility, or a personal doctor of the first party.
 7. A computerprogram product comprising a non-transitory computer readable storagemedium having a computer readable program stored therein, wherein thecomputer readable program, when executed on a data processing system,causes the data processing system to implement an emergency responsecognitive computing system, and further causes the data processingsystem to: identify, by the emergency response cognitive computingsystem, a first party about which a communication is being performedbetween a caller and the emergency response system; generate, by theemergency response cognitive computing system, in real-time, atranscript of the communication as the communication is being conducted;perform, by the emergency response cognitive computing system, naturallanguage processing on the transcript of the communication to identifyportions of content corresponding to medical concepts, wherein theportions of content corresponding to the medical concepts from thetranscript of the communication are identified by comparing each term oreach set of terms in the transcript of the communication to one or moreterms or set of terms in a medical condition library; process, by theemergency response cognitive computing system, patient information toidentify elements of the patient information referencing conceptscorresponding to the medical concepts; and output, by the emergencyresponse cognitive computing system, the elements of the patientinformation to a second party involved in the communication.
 8. Thecomputer program product of claim 7, wherein the first party isidentified based on information entered into the emergency responsesystem based on information provided by the caller.
 9. The computerprogram product of claim 7, wherein the first party is identified basedon automated caller identification.
 10. The computer program product ofclaim 7, wherein the emergency response cognitive computing system isintegrated into the emergency response system.
 11. The computer programproduct of claim 7, wherein the patient information is electronicmedical records of the patient.
 12. The computer program product ofclaim 7, wherein the second party is one or more of an emergencypersonnel on the other end of the communication, an emergency medicaltechnician assigned to respond to the first party, a medicalprofessional assigned to treat the patient once transported to anmedical facility, or a personal doctor of the first party.
 13. A dataprocessing system comprising: at least one processor; and at least onememory coupled to the at least one processor, wherein the at least onememory comprises instructions which, when executed by the at least oneprocessor, cause the at least one processor to implement an emergencyresponse cognitive computing system, and further cause the at least oneprocessor to: identify, by the emergency response cognitive computingsystem, a first party about which a communication is being performedbetween a caller and the emergency response system; generate, by theemergency response cognitive computing system, in real-time, atranscript of the communication as the communication is being conducted;perform, by the emergency response cognitive computing system, naturallanguage processing on the transcript of the communication to identifyportions of content corresponding to medical concepts, wherein theportions of content corresponding to the medical concepts from thetranscript of the communication are identified by comparing each term oreach set of terms in the transcript of the communication to one or moreterms or set of terms in a medical condition library; process, by theemergency response cognitive computing system, patient information toidentify elements of the patient information referencing conceptscorresponding to the medical concepts; and output, by the emergencyresponse cognitive computing system, the elements of the patientinformation to a second party involved in the communication.
 14. Thedata processing system of claim 13, wherein the first party isidentified based on information entered into the emergency responsesystem based on information provided by the caller.
 15. The dataprocessing system of claim 13, wherein the first party is identifiedbased on automated caller identification.
 16. The data processing systemof claim 13, wherein the patient information is electronic medicalrecords of the patient.
 17. The data processing system of claim 13,wherein the second party is one or more of an emergency personnel on theother end of the communication, an emergency medical technician assignedto respond to the first party, a medical professional assigned to treatthe patient once transported to an medical facility, or a personaldoctor of the first party.